# How do you interpret the results from ridge regression?

I started learning ridge regression in R. I applied the linear ridge regression to my full data set and got the following results.

gridge<-lm.ridge(divorce ~., data=divusa, lambda=seq(0,35,0.02))
select(gridge)
modified HKB estimator is 0.07693804
modified L-W estimator is 0.3088377
smallest value of GCV at 0.02 which.min(gridge$GCV) 0.02 2 round(coef(gridge)[2,-1],3) year unemployed femlab marriage birth military -0.195 -0.053 0.790 0.148 -0.118 -0.042 round(coef(g)[-1],3) year unemployed femlab marriage birth military -0.203 -0.049 0.808 0.150 -0.117 -0.043  Questions: 1. How do I interpret the results? 2. Do I have to do anything else for interpretation? ## 1 Answer Some things to look at when fitting the ridge regression regression coefficients for this fit: round(gridge$coef[, which(gridge$lambda ==.02)], 2)  ordinary least square fit: round(gridge$coef[, which(gridge\$lambda == 0)], 2)


The ridge regression centers and scales the predictors so you need to do the same when calculating the fit. You can add back the mean of the response.